4 citations · 7 across the 18 of their papers we have counts for
5 papers · 1 filter
Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection
Jia-Feng Cai, Zibo Chen, Xiao-Ming Wu +3
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works…
An Economic Framework for 6-DoF Grasp Detection
Xiao-Ming Wu, Jia-Feng Cai, Jian-Jian Jiang +3
Robotic grasping in clutters is a fundamental task in robotic manipulation. In this work, we propose an economic framework for 6-DoF grasp detection, aiming to economize the resour…
Grasp as You Say: Language-guided Dexterous Grasp Generation
Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing +5
This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However…
Dexterous Grasp Transformer
Guo-Hao Xu, Yi-Lin Wei, Dian Zheng +2
In this work, we propose a novel discriminative framework for dexterous grasp generation, named Dexterous Grasp TRansformer (DGTR), capable of predicting a diverse set of feasible…
Single-View Scene Point Cloud Human Grasp Generation
Yan-Kang Wang, Chengyi Xing, Yi-Lin Wei +2
In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observin…